10 Gb/s光纤通信系统一阶偏振模色散动态补偿系统反馈控制的实现

在偏振模色散(PMD)自动补偿技术中,如何根据反馈信号得到相应的控制信号,使补偿速度跟随偏振模色散变化始终是该技术的一个核心问题。提出了一种新颖的自适应抖动跟踪算法,完成了以微波信号为反馈的多自由度的一阶偏振模色散自动反馈补偿系统的跟踪补偿实验。算法成功地解决了传统算法在跟踪搜索过程中易陷入局部极值的问题,有效地克服了系统中的重要控制器件偏振控制器的磁滞现象以及动态补偿时跟踪搜索过程中易出现的瞬间恶化现象。实验结果表明该算法在对出现突发偏振模色散扰动后自动进行补偿的响应速度在ms量级,最快能达到1~2 ms。     

Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy

In order to accurately identify various types of ships and develop coastal defenses, a single feature extraction method based on slope entropy (SlEn) and a double feature extraction method based on SlEn combined with permutation entropy (SlEn&PE) are proposed. Firstly, SlEn is used for the feature extraction of ship-radiated noise signal (SNS) compared with permutation entropy (PE), dispersion entropy (DE), fluctuation dispersion entropy (FDE), and reverse dispersion entropy (RDE), so that the effectiveness of SlEn is verified, and SlEn has the highest recognition rate calculated by the k-Nearest Neighbor (KNN) algorithm. Secondly, SlEn is combined with PE, DE, FDE, and RDE, respectively, to extract the feature of SNS for a higher recognition rate, and SlEn&PE has the highest recognition rate after the calculation of the KNN algorithm. Lastly, the recognition rates of SlEn and SlEn&PE are compared, and the recognition rates of SlEn&PE are higher than SlEn by 4.22%. Therefore, the double feature extraction method proposed in this paper is more effective in the application of ship type recognition.     

A Probabilistic Re-Intepretation of Confidence Scores in Multi-Exit Models

In this paper, we propose a new approach to train a deep neural network with multiple intermediate auxiliary classifiers, branching from it. These ‘multi-exits’ models can be used to reduce the inference time by performing early exit on the intermediate branches, if the confidence of the prediction is higher than a threshold. They rely on the assumption that not all the samples require the same amount of processing to yield a good prediction. In this paper, we propose a way to train jointly all the branches of a multi-exit model without hyper-parameters, by weighting the predictions from each branch with a trained confidence score. Each confidence score is an approximation of the real one produced by the branch, and it is calculated and regularized while training the rest of the model. We evaluate our proposal on a set of image classification benchmarks, using different neural models and early-exit stopping criteria.     

A control volume finite element method for three-dimensional three-phase flows

A novel control volume finite element method with adaptive anisotropic unstructured meshes is presented for three-dimensional three-phase flows with interfacial tension. The numerical framework consists of a mixed control volume and finite element formulation with a new P₁DG-P₂ elements (linear discontinuous velocity between elements and quadratic continuous pressure between elements). A “volume of fluid” type method is used for the interface capturing, which is based on compressive control volume advection and second-order finite element methods. A force-balanced continuum surface force model is employed for the interfacial tension on unstructured meshes. The interfacial tension coefficient decomposition method is also used to deal with interfacial tension pairings between different phases. Numerical examples of benchmark tests and the dynamics of three-dimensional three-phase rising bubble, and droplet impact are presented. The results are compared with the analytical solutions and previously published experimental data, demonstrating the capability of the present method.     

Flume testing of passively adaptive composite tidal turbine blades under combined wave and current loading

The tidal energy industry is progressing rapidly, but there are still barriers to overcome to realise the commercial potential of this sector. Large magnitude and highly variable loads caused by waves acting on the turbine are of particular concern. Composite blades with in-built bend-twist elastic response may reduce these peak loads, by passively feathering with increasing thrust. This could decrease capital costs by lowering the design loads, and improve robustness through the mitigation of pitch mechanisms. In this study, the previous research is extended to examine the performance of bend-twist blades in combined wave–current flow, which will frequently be encountered in the field. A scaled 3 bladed turbine was tested in the flume at IFREMER with bend-twist composite blades and equivalent rigid blades, sequentially under current and co-directional wave–current cases. In agreement with previous research, when the turbine was operating in current alone at higher tip speed ratios the bend-twist blades reduced the mean thrust and power compared to the rigid blades. Under the specific wave–current condition tested the average loads were similar on both blade sets. Nevertheless, the bend-twist blades substantially reduced the magnitudes of the average thrust and torque fluctuations per wave cycle, by up to 10% and 14% respectively.     

三维非连续变形分析方法碰撞分析及其应用

将具有完备动力学理论的非连续变形分析(DDA)方法应用于块体碰撞研究。基于三维DDA(3D DDA)方法,按时步输出块体碰撞过程速度变化和接触嵌入量,进而得到块体碰撞恢复系数、冲量、冲击力。以此为参考指标,采用斜抛、面-面对心等碰撞模型,验证3D DDA方法模拟块体碰撞的有效性,并将3D DDA方法应用于多米诺骨牌倾倒、滚石边坡成灾及防护等算例分析,探讨了多米诺骨牌倾倒机制、滚石启动及运动行为、滚石灾害防护方案。结果表明:多米诺骨牌间距越大,同一块体被碰撞时间越迟,其最终稳定时间也越迟,与下一块体碰撞的动能越大;滚石运动呈侧向平动及转动三维运动特征,每一次碰撞,均引起动能、轨迹或状态的显著变化;滚石拦挡设施弹簧刚度越大,越先达到最大冲击力,最大冲击力随弹簧刚度的增加而减小;可结合树木阻挡效应,耗散滚石动能,降低滚石飞跃高度,使滚石灾害减轻或控制在防护范围以内。     

基于持续同调的边坡土颗粒应力链失稳分析

采用颗粒离散元方法和持续同调理论,研究了内排土场堆叠至不同高度时的边坡稳定性。为便于研究,现采用一水平金属板向下施加压力,代替不同厚度土层的重力荷载,对边坡在竖向荷载作用下的失稳破坏过程进行了颗粒离散元模拟。研究了二维边坡土颗粒速度总矢量、边坡失稳破坏时滑移开裂面的角度以及边坡坡顶y方向的平均速度等宏观响应过程,并构建了自然堆积下边坡堆积体颗粒的法向力链无向网络模型。最后,用持续同调方法对边坡坡顶颗粒接触力链网络的拓扑特征进行分析,获得条码图,建立了岩体结构持续同调特征与失稳演化的关系。本文为研究边坡失稳拓扑识别提供了一种新方法,从而可以有效预测边坡的失稳破坏。     

语音通信降噪研究

语音通信系统中,语音通过信道传输将不可避免地引入码间串扰和信号畸变,同时受到噪声污染。本文在分析自适应盲均衡算法CMA(constant modulus algorithm）和改进盲均衡算法的基础上,考虑到自适应盲均衡技术在语音噪声控制方面能力有限,将自适应盲均衡技术与小波包掩蔽阈值降噪算法联合使用,形成一种基带语音增强新方法。仿真试验结果显示自适应盲均衡技术可以使星座图变得清晰而紧凑,有效减小误码率。研究证实该方法在语音信号ISI和畸变严重情况下,在白噪及有色噪声不同的噪声环境中都具有稳定的降噪能力,消噪同时可获得汉语普通话良好的听觉效果。     

EicC对撞光学设计

EicC是中国科学院近代物理研究所计划建造的中国电子-离子对撞机装置,该对撞机质心能位于20 GeV附近,是研究海夸克的最佳能量窗口,同时还可研究胶子和价夸克。EicC对撞粒子为高极化率质子和电子束团,质子环pRing采用八字环设计方案,可以更好地保持极化质子束团极化率,电子环eRing采用跑道形环设计方案,可以更好地利用隧道空间。该装置电子束流能量中心值为3.5 GeV,电子束RMS发射度为水平方向60 nm·rad,垂直方向60 nm·rad,对撞点b函数为水平方向0.4 m,垂直方向0.12 m;质子束流能量中心值20 GeV,质子束RMS发射度为水平方向300 nm·rad,垂直方向180 nm·rad,对撞点b函数为水平方向0.08 m,垂直方向0.04 m,设计亮度2×1033 cm–2s–1。EicC采用双对撞区非对称光学设计,通过对EicC不同色品补偿方案的研究,最终确定了弧区加短直线节共同补偿的色品补偿方案;通过研究对撞点处b函数以及对撞点间相移对动力学孔径的影响,最终得到pRing动力学孔径大于8 s(s为束团RMS尺寸)、eRing动力学孔径大于20 s,满足大于束团尺寸6 s的要求。     

Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.